BibSonomyburst/user/nepomuk/analysisBibSonomy RSS feed for /user/nepomuk/analysis2012-02-16T12:14:04+01:00http://www.bibsonomy.org/bibtex/2e64d14f3207766f4afc65983fa759ffe/nepomuknepomuk2009-05-13T09:36:02+02:002008 analysis engine from:jaeschke information l3s logsonomy myown network retrieval search social wp5 <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>HT &#039;08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, </em><em>page 157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed May 13 09:36:02 CEST 2009New York, NY, USAHT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia157--166Logsonomy - Social Information Retrieval with Logdata20082008 analysis engine from:jaeschke information l3s logsonomy myown network retrieval search social wp5 Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.http://www.bibsonomy.org/bibtex/218e8babe208fae2c0342438617b0ec31/nepomuknepomuk2009-01-05T13:51:02+01:002008 analysis concept folksonomy formal from:jaeschke l3s myown tagging trias wp5 <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Ganter">Bernhard Ganter</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em> <em>6(1):38--53</em> (<em>February 2008</em>)Mon Jan 05 13:51:02 CET 2009New YorkSemantic Web and Web 2.0Web Semantics: Science, Services and Agents on the World Wide WebFeb138--53Discovering Shared Conceptualizations in Folksonomies620082008 analysis concept folksonomy formal from:jaeschke l3s myown tagging trias wp5 Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomieshttp://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/nepomuknepomuk2008-06-20T14:42:46+02:00analysis engine from:jaeschke information l3s logsonomy network retrieval search social wp5 <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>HT &#039;08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, </em><em>page 157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Fri Jun 20 14:42:46 CEST 2008New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata2008analysis engine from:jaeschke information l3s logsonomy network retrieval search social wp5 Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.http://www.bibsonomy.org/bibtex/263901930c137df0c2dad84075c564b14/nepomuknepomuk2008-04-15T11:58:42+02:002008 analysis concept folksonomy formal from:jaeschke l3s myown tagging wp5 <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Ganter">Bernhard Ganter</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Web Semantics: Science, Services and Agents on the World Wide Web</em> <em>6(1):38--53</em> (<em>February 2008</em>)Tue Apr 15 11:58:42 CEST 2008Semantic Web and Web 2.0Web Semantics: Science, Services and Agents on the World Wide Webfeb138--53Discovering Shared Conceptualizations in Folksonomies620082008 analysis concept folksonomy formal from:jaeschke l3s myown tagging wp5 Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomieshttp://www.bibsonomy.org/bibtex/22cbbe0790e81d5af7927ba51098fa126/nepomuknepomuk2007-06-13T16:58:36+02:002007 analysis bibsonomy bookmarking fca folksonomy from:jaeschke iccs myown social trias <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Proceedings of the 15th International Conference on Conceptual Structures, </em><em>Sheffield, England, </em>(<em>July 2007</em>)Wed Jun 13 16:58:36 CEST 2007Sheffield, EnglandProceedings of the 15th International Conference on Conceptual StructuresJulyAnalysis of the Publication Sharing Behaviour in {BibSonomy}20072007 analysis bibsonomy bookmarking fca folksonomy from:jaeschke iccs myown social trias BibSonomy is a web-based social resource sharing system which allows users to organise and share bookmarks and publications in a collaborative manner. In this paper we present the system, followed by a description of the insights in the structure of its bibliographic data that we gained by applying techniques we developed in the area of Formal Concept Analysis.http://www.bibsonomy.org/bibtex/211b2a59a568d246d7f36cb68169a464a/nepomuknepomuk2007-03-22T18:29:28+01:002006 analysis folksonomies folksonomy from:pchirita l3s myown nepomuk network semantic wp5 <span class="authorEditorList"><a href="/author/Schmitz">Christoph Schmitz</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Jäschke">Robert Jäschke</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Data Science and Classification. Proceedings of the 10th IFCS Conf., </em><em>page 261--270. </em><em>Heidelberg, </em><em>Springer, </em>(<em>July 2006</em>)Thu Mar 22 18:29:28 CET 2007HeidelbergData Science and Classification. Proceedings of the 10th IFCS Conf.July261--270Studies in Classification, Data Analysis, and Knowledge OrganizationMining Association Rules in Folksonomies20062006 analysis folksonomies folksonomy from:pchirita l3s myown nepomuk network semantic wp5 Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.http://www.bibsonomy.org/bibtex/2c44763991d44182c53606a2c93054f26/nepomuknepomuk2007-03-22T18:29:28+01:002005 analysis from:pchirita iswc l3s myown nepomuk network proceedings semantic semna sna workshop wp5 <span class="authorEditorList"><a href="/author/Stumme">Gerd Stumme</a>, <a href="/author/Hoser">Bettina Hoser</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, and <a href="/author/Alani">Harith Alani</a> (Eds.). </span><em>Aachen, </em><em>CEUR Proceedings, </em>(<em>2005</em>)Thu Mar 22 18:29:28 CET 2007AachenProceedings of the First Workshop on Semantic Network Analysis 20052005 analysis from:pchirita iswc l3s myown nepomuk network proceedings semantic semna sna workshop wp5 Collocated with the 4th International Semantic Web Conference